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Topic · Computer Vision and Pattern Recognition

Currency Recognition and Detection

Currency Recognition and Detection is a research topic within Computer Vision and Pattern Recognition. Science Explorer counts 6.6k research works in it since 1950. 14.6% of them reached the world's top 10% most cited for their field and year.

This cluster of papers focuses on automated recognition and authentication of paper currency and coins using techniques such as image processing, deep learning, neural networks, and feature extraction. The research covers various aspects including banknote security, counterfeit detection, and assistance for the visually impaired.

  • Currency Recognition
  • Banknote Recognition
  • Coin Classification
  • Image Processing
  • Deep Learning
  • Neural Network
  • Counterfeit Detection
  • Visually Impaired
  • Feature Extraction
  • Pattern Recognition
Research works
6.6k
fractional, since 1950
In the world top 10%
971
per year above
Top-10% rate
14.6%
share of its works in the world top 10%
Growth, 2013–17 → 2018–22
+242%
the tick is no change

Which countries lead Currency Recognition and Detection research?

By volume, India and China publish the most (963 and 574 works in 2022–2025).

By volume, 2022–2025

  1. 1 India 963 works
  2. 2 China 574 works
  3. 3 United States 153 works
  4. 4 Indonesia 120 works
  5. 5 Türkiye 83 works
  6. 6 ?? 80 works
  7. 7 Malaysia 67 works
  8. 8 Iraq 58 works
  9. 9 Bangladesh 51 works
  10. 10 United Kingdom 47 works

How concentrated that is

The same countries as shares of everything the list above accounts for. A node where two countries do two thirds of the work and one spread evenly across twelve read alike as a ranking and not at all alike here.

India: 43.9%China: 26.1%United States: 7.0%Indonesia: 5.5%6 others listed: 17.6%44%largest
India963 · 43.9%China574 · 26.1%United States153 · 7.0%Indonesia120 · 5.5%6 others listed386 · 17.6%

Shares of the rows listed above, not of the whole node.

Which institutions lead Currency Recognition and Detection research?

By volume in 2022–2025, Saveetha University publishes the most Currency Recognition and Detection research, followed by Vellore Institute of Technology University and SRM Institute of Science and Technology.

By volume, 2022–2025

  1. 1 Saveetha UniversityIndia 40 works
  2. 2 Vellore Institute of Technology UniversityIndia 27 works
  3. 3 SRM Institute of Science and TechnologyIndia 27 works
  4. 4 Chitkara UniversityIndia 26 works
  5. 5 Graphic Era UniversityIndia 19 works
  6. 6 Amrita Vishwa VidyapeethamIndia 19 works
  7. 7 Chandigarh UniversityIndia 18 works
  8. 8 Koneru Lakshmaiah Education FoundationIndia 15 works
  9. 9 Sathyabama Institute of Science and TechnologyIndia 12 works
  10. 10 Institute of EngineeringNepal 12 works

Where is Currency Recognition and Detection research done?

The largest centres of Currency Recognition and Detection research in 2022–2025 are Chennai (India), Beijing (China), Bengaluru (India) and Pune (India). Among places with at least 20 works in it, it is an unusually large share of all research in Vijayawada and Greater Noida.

Largest cities, 2022–2025

  1. 1 Chennai India 122 works
  2. 2 Beijing China 101 works
  3. 3 Bengaluru India 52 works
  4. 4 Pune India 50 works
  5. 5 Coimbatore India 44 works
  6. 6 Shanghai China 37 works
  7. 7 Dehra Dūn India 33 works
  8. 8 Dhaka Bangladesh 32 works
  9. 9 Chandigarh India 30 works
  10. 10 Hyderabad India 30 works

Where it is the local speciality

  1. VijayawadaIN · 26.8 works20×
  2. Greater NoidaIN · 25.3 works14×
← less than its size predicts1×more →

Location quotient: how much more of its research is in Currency Recognition and Detection than the world average.

See Currency Recognition and Detection on the map

Where is the best place to study Currency Recognition and Detection?

Among universities, judged by research, Vellore Institute of Technology University, Graphic Era University and Saveetha University score highest, combining excellence, specialisation, size, growth and international reach. Research strength is one signal when choosing where to study; it does not measure teaching.

0%10%20%30%mean 15.78%fractional works in this node (log) →share in the world top 10% →Vellore Institute of Technology University: 27, 28.1%Graphic Era University: 19, 22.8%Saveetha University: 40, 8.0%Chitkara University: 26, 14.3%Savitribai Phule Pune University: 10, 23.7%Koneru Lakshmaiah Education Foundation: 16, 16.3%Chandigarh University: 18, 13.9%SRM Institute of Science and Technology: 27, 8.9%Amrita Vishwa Vidyapeetham: 19, 13.0%Institute of Engineering: 12, 8.8%Vellore Institute of…Graphic Era UniversityChitkara UniversitySaveetha University
above the meannear itbelow it

One dot per university in the table below. The upper left is the interesting corner: small places doing unusually strong work.

#UniversityScoreTop 10%SpecialisationWorksGrowth
1 Vellore Institute of Technology UniversityIndia 84.728.1%9.5×27 +541.2%
2 Graphic Era UniversityIndia 69.622.8%19.5×19 —
3 Saveetha UniversityIndia 63.88.0%14.4×40 —
4 Chitkara UniversityIndia 63.614.3%23.0×26 —
5 Savitribai Phule Pune UniversityIndia 60.023.7%13.2×10 —
6 Koneru Lakshmaiah Education FoundationIndia 59.216.3%19.0×16 —
7 Chandigarh UniversityIndia 58.313.9%11.6×18 —
8 SRM Institute of Science and TechnologyIndia 57.38.9%11.1×27 —
9 Amrita Vishwa VidyapeethamIndia 57.113.0%12.5×19 —
10 Institute of EngineeringNepal 51.08.8%21.2×12 +97.2%

Universities only. Score blends excellence (30%), specialisation (25%), size (20%), growth (15%) and international reach (10%), 2015–2022; growth compares 2010–14 with 2015–19.

Is Currency Recognition and Detection research growing?

Output in 2018–2022 was 242% higher than in 2013–2017, peaking in 2024. The fastest-growing topics are Currency Recognition and Detection.

19801990200020102020
grewheldshrank

The same series as a ribbon — one cell per year, darker for more. The line above answers how much; this answers when.

Which topics inside it are moving

Growth and decline on one axis around a shared zero. Two lists side by side hide the thing that matters: whether the growth dwarfs the decline, or the other way round.